import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[Any]:

							'''simple docstring'''


							torch.manual_seed(0		)
							__lowercase					     =				UNetaDModel(
							    block_out_channels=(32, 64) ,  layers_per_block=2 ,  sample_size=32 ,  in_channels=3 ,  out_channels=3 ,  down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') ,  up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') ,  )
							return model





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							__lowercase					     =				self.dummy_uncond_unet
							__lowercase					     =				ScoreSdeVeScheduler()

							__lowercase					     =				ScoreSdeVePipeline(unet=lowerCAmelCase__ ,  scheduler=lowerCAmelCase__		)
							sde_ve.to(lowerCAmelCase__		)
							sde_ve.set_progress_bar_config(disable=lowerCAmelCase__		)

							__lowercase					     =				torch.manual_seed(0		)
							__lowercase					     =				sde_ve(num_inference_steps=2 ,  output_type='''numpy''' ,  generator=lowerCAmelCase__		).images

							__lowercase					     =				torch.manual_seed(0		)
							__lowercase					     =				sde_ve(num_inference_steps=2 ,  output_type='''numpy''' ,  generator=lowerCAmelCase__ ,  return_dict=lowerCAmelCase__		)[
							    0
							]

							__lowercase					     =				image[0, -3:, -3:, -1]
							__lowercase					     =				image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							__lowercase					     =				np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]		)

							assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2




@slow
@require_torch
class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							__lowercase					     =				'''google/ncsnpp-church-256'''
							__lowercase					     =				UNetaDModel.from_pretrained(lowerCAmelCase__		)

							__lowercase					     =				ScoreSdeVeScheduler.from_pretrained(lowerCAmelCase__		)

							__lowercase					     =				ScoreSdeVePipeline(unet=lowerCAmelCase__ ,  scheduler=lowerCAmelCase__		)
							sde_ve.to(lowerCAmelCase__		)
							sde_ve.set_progress_bar_config(disable=lowerCAmelCase__		)

							__lowercase					     =				torch.manual_seed(0		)
							__lowercase					     =				sde_ve(num_inference_steps=10 ,  output_type='''numpy''' ,  generator=lowerCAmelCase__		).images

							__lowercase					     =				image[0, -3:, -3:, -1]

							assert image.shape == (1, 2_56, 2_56, 3)

							__lowercase					     =				np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]		)

							assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				word.split()

				def justify(lowercase				,     lowercase				,     lowercase				) -> str:
								__lowercase					     =				max_width - width
								__lowercase					     =				len(lowercase				)
								if len(lowercase				) == 1:
												# if there is only word in line
												# just insert overall_spaces_count for the remainder of line
												return line[0] + " " * overall_spaces_count
								else:
												__lowercase					     =				words_count - 1
												# num_spaces_between_words_list[i] : tells you to insert
												# num_spaces_between_words_list[i] spaces
												# after word on line[i]
												__lowercase					     =				spaces_to_insert_between_words * [
												    overall_spaces_count // spaces_to_insert_between_words
												]
												__lowercase					     =				(
												    overall_spaces_count % spaces_to_insert_between_words
												)
												# distribute spaces via round robin to the left words
												for i in range(lowercase				):
																num_spaces_between_words_list[i] += 1
												__lowercase					     =				[]
												for i in range(lowercase				):
																# add the word
																aligned_words_list.append(line[i]				)
																# add the spaces to insert
																aligned_words_list.append(num_spaces_between_words_list[i] * ''' '''				)
												# just add the last word to the sentence
												aligned_words_list.append(line[-1]				)
												# join the aligned words list to form a justified line
												return "".join(lowercase				)

				__lowercase					     =				[]
				__lowercase					     =				[]
				__lowercase					     =				0
				for word in words:
								if width + len(lowercase				) + len(lowercase				) <= max_width:
												# keep adding words until we can fill out max_width
												# width = sum of length of all words (without overall_spaces_count)
												# len(word) = length of current word
												# len(line) = number of overall_spaces_count to insert between words
												line.append(lowercase				)
												width += len(lowercase				)
								else:
												# justify the line and add it to result
												answer.append(justify(lowercase				,     lowercase				,     lowercase				)				)
												# reset new line and new width
												__lowercase     ,	__lowercase					     =				[word], len(lowercase				)
				__lowercase					     =				max_width - width - len(lowercase				)
				answer.append(''' '''.join(lowercase				) + (remaining_spaces + 1) * ''' '''				)
				return answer


if __name__ == "__main__":
		from doctest import testmod

		testmod()